针对语音模型持续学习遗忘问题，提出高效参数更新方法。
Parameter-Efficient Continual Learning for Automatic Speech Recognition
- 按奇异值分权重矩阵为头尾部分，仅在低能量尾部做旋转式微调。
- 实验显示遗忘显著减少，整体识别性能优于现有方法。
- 适合资源有限场景下的语音模型持续优化，尤其适配大模型。
语音基础模型在通用自动语音识别（ASR）中表现强劲，但其庞大体量与顺序微调导致的灾难性遗忘，要求高效的参数化与正则化训练方法，推动了参数高效持续学习（PECL）的发展。尽管PECL在NLP和视觉领域已广泛研究，但在语音识别中仍较少被关注。本文提出一种基于近期参数高效微调进展的简单而有效的PECL方法：根据奇异值将预训练权重矩阵分为头尾子空间，并将适应限制在低能量尾子空间内的近似旋转，以保留主要成分并减少遗忘。对于后续任务，通过权重平均合并旋转操作，进一步提升记忆保持能力。在两个基准测试上的实验表明，该方法相比最新PECL基线，显著减少了遗忘并实现了更优的整体性能。
原文摘要 · Abstract (English)
Speech foundation models enable strong general-purpose ASR and are attractive for downstream adaptation. However, their size and the catastrophic forgetting induced by sequential fine-tuning demand parameter-efficient and regularized training methods, motivating parameter-efficient continual learning (PECL). While PECL has been widely studied in NLP and vision, it has received less attention in ASR. In this paper, we propose a simple yet effective PECL method based on recent advances in parameter-efficient fine-tuning for ASR. We partition pretrained weight matrices into head and tail subspaces according to singular values and restrict adaptation to approximate rotations within the low-energy tail subspace, preserving dominant components and reducing forgetting. For subsequent tasks, rotations are combined via weight averaging to further improve retention. Experiments on two benchmarks demonstrate reduced forgetting and superior overall performance compared to recent PECL baselines.
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